An Integrated Analysis of Prognostic and Immune Infiltrates for Hub Genes as Potential Survival Indicators in Patients with Lung Adenocarcinoma

Abstract Objective: Lung adenocarcinoma is one of the major subtypes of lung cancer. However, the prognosis of individuals with LUAD is still not promising. Therefore, this research aims to discover useful biomarkers to enhance the treatment and diagnosis of LUAD.Methods: GEO2R was used to identify common up-regulated DEGs in the GSE32863, GSE40791 and GSE75037. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis. Metascape was also utilized to construct the PPI network, and the MCODE plug-in was employed to filter important subnetworks. The prognosis and expression levels of the hub genes were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The Timer database was utilized to confirm the correlation between immune cells infiltration and the expression levels of hub genes in LUAD tissues.Results: This research discovered 307 common up-regulated DEGs, and gene ontology and pathway enrichment analysis indicated that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF and KNTC1. The seven hub genes were highly expressed in LUAD tissues and had a poor prognosis. AURKB, CCNB2, and CDC20 were inversely associated with B and CD4+ T cells. CDCA5, CDCA8, and CENPF have a substantially negative correlation with B Cell, but positive correlation with Neutrophil. Conclusions: This research demonstrates that increased expression of seven hub genes is associated with worse prognosis for LUAD patients. Additionally, immune cells infiltrating LUAD tissues may serve as a regulating mechanism.

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Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

Science Progress ◽

10.1177/00368504211018053 ◽

2021 ◽

Vol 104 (3) ◽

pp. 003685042110180

Author(s):

Xiao Lin ◽

Meng Zhou ◽

Zehong Xu ◽

Yusheng Chen ◽

Fan Lin

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

High Risk ◽

Lung Adenocarcinoma ◽

Candidate Genes ◽

Postoperative Recurrence ◽

High Expression ◽

Ppi Network ◽

Hub Genes ◽

Expression Levels

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein–protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

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MFN2 might be a risk factor for lung adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.e13007 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. e13007-e13007

Author(s):

Yuqing Lou ◽

Yanwei Zhang ◽

Jianlin Xu ◽

Ping Gu ◽

Wei Zhang ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Lung Adenocarcinoma ◽

A549 Cells ◽

Enrichment Analysis ◽

Tumor Formation ◽

Normal Lung ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Functional Pathway

e13007 Background: Genetic mutations in Mitofusin-2(MFN2) interrupt mitochondrial fusion and cause the untreatable neurodegenerative condition Charcot-Marie-Tooth disease type 2A( Nature, December 2016). MFN2 was initially identified as a hypertension-associated gene and implicated in the pathogenesis of multiple cancer types. However, underlying mechanisms of MFN2 in lung adenocarcinoma was unclear. Methods: MFN2 expression at protein level was examined in 30 pair lung adenocarcinoma/adjacent normal lung samples with immunohistochemistry staining. Then MFN2 knocked down in human lung adenocarcinoma cells A549 with lentiviral-mediated shRNA strategy. The effects of MFN2 knockdown on cell proliferation, cell cycle process, cell migration and invasion was investigated in A549 cells. MFN2-knockdown induced gene expression changes was analyzed by microarray assay and then functional pathway enrichment analysis was performed to identify critical pathways involved in MFN2-mediated lung adenocarcinoma development. The expression changes of downstream factors were determined by western blot. Furthermore, tumor models in nude mice were generated. Tumor formation and progression in these mice were analyzed. Results: As compared to adjacent normal lung tissues, MFN2 expression was significantly higher in lung adenocarcinoma tissues with positive MFN2 signals in 90% (27/30) lung adenocarcinoma tissues and only in 26.7% (8/30) adjacent normal tissues. Furthermore, MFN2 knockdown inhibited cell proliferation, induced cell cycle arrest and blocked invasion behavior in A549 cells. MFN2-knockdown induced gene expression changes in A549 cells was analyzed by microarray assay. Functional pathway enrichment analysis revealed that 6 pathways were enriched in deregulated genes including Cell cycle, DNA replication, ECM-receptor interaction, Focal adhesion, MAPK signaling pathway and Chemokine signaling pathway. Downregulation of RAP1A and upregulation of RALB and ITGA2 identified in MFN2-knockdown cells by microarray analysis were confirmed by western blot. In vivo, tumor formation and progression in nude mice showed that MFN2 knockdown reduced tumorigenesis of A549 cells. Conclusions: MFN2 overexpression run a risk of lung adenocarcinoma.

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Identification of Differentially Expressed Genes and associated pathways common to Eyelid and Non-Ocular Basal Cell Carcinoma to understand the Molecular Biology of BCC

10.1101/2021.05.01.442022 ◽

2021 ◽

Author(s):

Perumal Jayaraj ◽

Seema Sen ◽

Pranjal Vats ◽

Shefali Dahiya ◽

Vanshika Mohindroo

Keyword(s):

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Basal Cell ◽

Expression Profiles ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Malignant Neoplasms ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Pathway Enrichment

Background: Eyelid BCC accounts for more than 90% of Eyelid malignant neoplasms. Various aberrant signalling pathways and genes in Non-Ocular BCC have been found whereas Eyelid bcc remains elusive. Objective: This study aims to find the common DEGs of Eyelid and Non-Ocular BCC using bioinformatic analysis and text mining to gain more insights into the molecular aspects common to both BCC non-ocular and Eyelid BCC and to identify common potential prognostic markers. Material and method: The Gene Expression profiles of Eyelid BCC (GSE103439) and Non-Ocular BCC (GSE53462) were obtained from the NCBI GEO database followed by identification of common DEGs. Protein-Protein interaction and Pathway Enrichment analysis of these screened genes was done using bioinformatic tools like STRING, Cytoscape and BiNGO, DAVID, KEGG respectively. Results: A total of 181 genes were found common in both datasets. A PPI network was formed for the screened genes and 20 HUB genes were sorted which included CTNNB1, MAPK14, BTRC, EGFR, ADAM17. Pathway enrichment of HUB genes showed that they were dysregulated in carcinogenic and apoptotic pathways that seem to play a role in the progression of both the BCC. Conclusion: The result and findings of bioinformatic analysis highlighted the molecular pathways and genes enriched in both Eyelid BCC as well as Non- Ocular BCC. The identified pathways should be studied further to recognise common molecular events that would lead to the progression of BCC. This may provide a window to explore the prognostic and therapeutic strategies common to both BCC. Keywords: Basal cell carcinoma (BCC), Cancer, Microarray, Ophthalmology, Tumour marker

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Identification of Potential Autophagy-Related Genes in Steroid-Induced Osteonecrosis of the Femoral Head

10.21203/rs.3.rs-1066425/v1 ◽

2021 ◽

Author(s):

XueZhen LIANG ◽

Di LUO ◽

Yan-Rong CHEN ◽

Jia-Cheng LI ◽

Bo-Zhao YAN ◽

...

Keyword(s):

Femoral Head ◽

Correlation Analysis ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment

Abstract Purpose: Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people. Previous experimental studies had shown that autophagy might be involved in the pathological process of SONFH, but the pathogenesis of autophagy in SONFH remained unclear. We aim to identify and validate the key potential autophagy-related genes of SONFH to further illustrate the mechanism of autophagy in SONFH through bioinformatics analysis. Methods: The mRNA expression profile dataset GSE123568 was download from Gene Expression Omnibus (GEO) database, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples. The autophagy-related genes were obtained from the Human Autophagy Database (HADb). The autophagy-related genes of SONFH were screened by intersecting GSE123568 dataset with autophagy genes. The differentially expressed autophagy-related genes of SONFH were identified by R software. Besides, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted for the differentially expressed autophagy-related genes of SONFH by R software. Then, the correlation analysis between the expression levels of differentially expressed autophagy-related genes of SONFH was confirmed by R software. Moreover, the protein–protein interaction (PPI) network were analyzed by the Search Tool for the Retrieval of Interacting Genes (STRING), and the significant gene cluster modules were identified by the MCODE Cytoscape plugin, and hub genes of differentially expressed autophagy-related genes of SONFH were screened by the CytoHubba Cytoscape plugin. Finally, the expression levels of hub genes of differentially expressed autophagy-related genes of SONFH was validated in hip articular cartilage specimens from necrosis femur head (NFH) by GSE74089 dataset. Results: A total of 34 differentially expressed autophagy-related genes were identified between the peripheral blood of SONFH samples and non-SONFH Samples based on the defined criteria, including 25 up-regulated genes and 9 down-regulated genes. The GO and KEGG pathway enrichment analysis revealed that these 34 differentially expressed autophagy-related genes of SONFH were concentrated in death domain receptors, FOXO signaling pathway and apoptosis. The correlation analysis revealed a significant correlation among the 34 differentially expressed autophagy-related genes of SONFH. The PPI results demonstrated that the 34 differentially expressed autophagy-related genes interacted with each other. There were 10 hub genes identified by the MCC algorithms of Cytohubba. The results of GSE74089 dataset showed TNFSF10, PTEN and CFLAR were significantly upregulated while BCL2L1 were significantly downregulated in the hip cartilage specimens, which were consistent with the GSE123568 dataset. Conclusions: There were 34 potential autophagy-related genes of SONFH identified using bioinformatics analysis. TNFSF10, PTEN, CFLAR and BCL2L1 might serve as potential drug targets and biomarkers by regulating autophagy. These results would expand new insights into the autophagy-related understanding of SONFH and might be useful in the diagnosis and prognosis of SONFH.

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Gene Ontology and Pathway Enrichment Analysis

10.1007/978-981-16-3993-7_12 ◽

2021 ◽

pp. 257-279

Author(s):

Manoj Kumar Gupta ◽

Gayatri Gouda ◽

S. Sabarinathan ◽

Ravindra Donde ◽

Goutam Kumar Dash ◽

...

Keyword(s):

Gene Ontology ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment

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Immunological analysis and differential genes screening of venous thromboembolism

Hereditas ◽

10.1186/s41065-020-00166-6 ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Li-Na Gao ◽

Qiang Li ◽

Jian-Qin Xie ◽

Wan-Xia Yang ◽

Chong-Ge You

Keyword(s):

Venous Thromboembolism ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

New Ideas ◽

Gene Expression Profile Data ◽

Differential Genes

Abstract Purpose To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

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Deciphering Expression Profiling and Functional Signatures of Individualized Obesity-Associated Genes in Ossification of Ligamentum Flavum Through RNA-Sequence Data Mining

10.21203/rs.3.rs-955257/v1 ◽

2021 ◽

Author(s):

Baoliang Zhang ◽

Lei Yuan ◽

Guanghui Chen ◽

Xi Chen ◽

Xiaoxi Yang ◽

...

Keyword(s):

Correlation Analysis ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Immune Cells ◽

Ligamentum Flavum ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Ossification Of Ligamentum Flavum

Abstract Background: Obese individuals predispose to ossification of ligamentum flavum (OLF), whereas the underlying connections between obesity phenotype and OLF pathomechanism are not fully understood, especially during early life. This study aimed to explore obesity-associated genes and their functional signatures in OLF. Methods: Gene microarray expression data related to OLF were downloaded from the GSE106253 dataset in the Gene Expression Omnibus (GEO) database. The potential obesity-related differentially expressed genes (ORDEGs) in OLF were screened. Then, gene-ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied for these genes. Furthermore, protein-protein interactions (PPI) were used to identify hub ORDEGs, and Metascape was used to further verify the key signaling pathways and immune-related function signatures of hub ORDEGs. Finally, correlation analysis of hub ORDEGs and identified OLF-related infiltrating immune cells (OIICs) was constructed to understand the possible mechanical link among obesity, immune response and OLF. Results: OLF-related differentially expressed genes and 2051 obesity-related genes from four databases were intersected to obtain 99 ORDEGs, including 54 upregulated and 55 downregulated genes. GO and KEGG analysis revealed that these genes were mainly involved in metabolism, inflammation and immune-related biological functions and pathways. A PPI network was established to determine 14 hub genes (AKT1, CCL2, CCL5, CXCL2, ICAM1, IL10, MYC, PTGS2, SAA1, SOCS1, SOCS3, STAT3, TNFRSF1B and VEGFA). The co-expression network demonstrated that this module was associated with cellular response to biotic stimulus, regulation of inflammatory response, regulation of tyrosine phosphorylation of STAT protein. Furthermore, Metascape functional annotations showed that hub genes were mainly involved in receptor signaling pathway via JAK-STAT, response to TNF and regulation of defense response, and their representative enriched pathways were TNF, adipocytokine and JAK-STAT signaling pathways. Subgroup analysis indicated that T cell activation might be potential immune function processes involved, and correlation analysis revealed that cDCs, memory B-cells and preadipocytes were highly correlated infiltrating immune cells. Conclusions: Our study deciphered individualized obesity-associated gene signature for the first time, which may facilitate exploring the underlying cellular and molecular pathogenesis and novel therapeutic targets of obesity-related early-onset OLF.

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Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules

BMC Endocrine Disorders ◽

10.1186/s12902-021-00718-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

G. Prashanth ◽

Basavaraj Vastrad ◽

Anandkumar Tengli ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Target Genes ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Pathway Enrichment ◽

Drug Molecules

Abstract Background Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. Methods To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. Results A total of 820 DEGs were identified between healthy obese and metabolically unhealthy obese, among 409 up regulated and 411 down regulated genes. The GO enrichment analysis results showed that these DEGs were significantly enriched in ion transmembrane transport, intrinsic component of plasma membrane, transferase activity, transferring phosphorus-containing groups, cell adhesion, integral component of plasma membrane and signaling receptor binding, whereas, the REACTOME pathway enrichment analysis results showed that these DEGs were significantly enriched in integration of energy metabolism and extracellular matrix organization. The hub genes CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, which might play an essential role in obesity associated type 2 diabetes mellitus was further screened. Conclusions The present study could deepen the understanding of the molecular mechanism of obesity associated type 2 diabetes mellitus, which could be useful in developing therapeutic targets for obesity associated type 2 diabetes mellitus.

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Immunological Analysis and Differential Genes Screening of Venous Thromboembolism

10.21203/rs.3.rs-33006/v1 ◽

2020 ◽

Author(s):

Li-Na Gao ◽

Qiang Li ◽

Jian-Qin Xie ◽

Wan-Xia Yang ◽

Chong-Ge You

Keyword(s):

Venous Thromboembolism ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

New Ideas ◽

Gene Expression Profile Data ◽

Differential Genes

Abstract Purpose: To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods: The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results: Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions: Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

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